I'm analyzing the performance of 3 parameterizations of the same algorithm under the same dataset. This performance is user-evaluated under a single Likert-type item (5-values). Given the ordinal nature of this data, I'm using a non-parametric test.
I'm trying to address the question: are there statistically significant preferences between the different (3) parameterizations?
For the general analysis, should I use a repeated-measures test (e.g. Friedman Test) or an independent test (e.g. Kruskal-Wallis Test)?
Furthermore, in the presence of a statistically significant difference among the groups, what post-hoc should I perform?